Historically, in sheet metal forming processes a set of shaped tools, such as a die and a punch, are used to deform sheets of metal to three-dimensional metal parts. This is accomplished by stamping the desired geometry and inducing sufficient in-plane strain for final product strength and stability. FIG. 1 schematically shows, in the left half, a top view of a sheet metal blank 1 placed between forming tools, schematically represented by a punch 2. After pressure is exerted by the punch 2 in order to deform the blank 1, it takes the shape of a formed part 3, shown in the right half of FIG. 1. The tools are tailored and process parameters adjusted using empirical rules and trial and error through a series of physical tryouts. They are then deployed to the actual production line where unfortunately their performance cannot be guaranteed as measurements are difficult or impossible and the forming process is usually poorly monitored.
The forming process design phase is today assisted by numerical simulations. These numerical simulations are performed on digital computers and usually employ the well-established finite element method. The simulation computer programs, given as input a set of parameters such as the tool geometry and the process parameters, produce a description of the geometry of a sheet metal part after the forming process as well as the distribution of state variables, such as thicknesses, strains and stresses. More recently, instead of empirically setting initial values to those parameters and using heuristic or optimization methods to achieve the desirable characteristics, engineers use stochastic simulations. Instead of performing a single finite element simulation using fixed parameters, random variables with appropriate intervals are defined, and a multitude of finite element simulations are performed using different parameter sets as described, e.g., in EP 1 623 287. Through such computer aided engineering techniques, tool geometries and process parameters are established that ideally will produce the desired part.
Inevitably, due to common cause (non-assignable, noise) and special cause (assignable) variation and approximations between the computer simulation and the actual tools and processes, the actual parts may not be identical to the simulated part. Therefore, it has to be shown that the manufactured set of tools along with the prescribed process parameters can produce the desired part to the desired quality during try-out or be modified to do so. Further, as the actual tools are deployed at the production site, the stamping process has to be calibrated and monitored in order to assure the desired quality.
Currently, simulation results are typically only used little, or not at all for reference and consultation during tryouts and production. Wang, C. T., Zhang, J. J., Goan, N., in Draw-in Map—A Road Map for Simulation-Guided Die Tryout and Stamping Process Control, Numisheet 2005 and U.S. Pat. No. 7,130,708 B2 describe a process where a so-called engineered metal draw-in map is used in the tryout phase. In FIG. 2, draw-in 4 is the displacement of the sheet metal blank outline 5 to the formed part outline 6 during the deformation of the blank. The draw-in 4 is related to the distribution of flange material 8 between the formed part outline 6 and the punch opening line 7. When used during tryouts, given the blank size and position, tool geometries and process parameters match those prescribed by the simulation, it is attempted to rework the tools in order to match the simulation prescribed draw-in at certain positions. Typically, tryout workers resort to adjusting the restraining force of the draw beads.
Despite the indisputable progress in design and improvement in the quality of formed products, important issues are not being addressed:
1. When using draw-in maps during tryout, it is not possible to determine how to adjust process parameters in order to achieve the desired draw-in from the simulation. The adjustment for the different parameters is determined empirically by varying them using a best-guess method. This trial and error procedure is costly and time consuming.2. During production, as opposed to the tryout phase, the actual draw-in is not monitored and compared against the simulated one. Process parameters, stress, strain, thickness distributions or other important quality and process control measures are also difficult to acquire and often neglected. A process can drift out of control without noticing and defective parts can end up in the assembly line.
Accordingly, a need exists for a methodology to determine the process parameters and state variables for a part and potential modifications to the process parameters in order to achieve the desired state variables.
The following references are related to this problem, but do not provide an adequate solution:
Optimization of draw-in for an automotive sheet metal part An evaluation using surrogate models and response surfaces; T. Jansson, A. Andersson, L. Nilsson; Journal of Materials Processing Technology 159 (2005) 426-434: Aan optimization of the draw-in of an automotive sheet metal part is presented, using response surface methodology (RSM) and space mapping technique. The optimization adjusts the draw bead restraining force in the model such that the draw-in in a Finite Element (FE) model corresponds to the draw-in in the physical process. The paper is directed to understanding of draw bead mechanics and to the improvement of simulation of the effects of draw bead geometry. For comparing simulation results with measurements from actual forming operations, draw-in is measured at a limited number of points along the part circumference. An optimisation is performed to find the set of draw bead parameters that minimizes the sum of differences between the measured and the simulated draw-in. The paper concludes that it was not possible to reach a perfect match between the optimised restraining force and the actual restraining force in the tool with this method. This is attributed to the discrepancy in measurements of the draw beads or the draw-in, differences in measuring the draw-in in reality and simulations and variations in material properties and friction properties. The method cannot be used for quality control during production.
Sheet metal forming global control system based on artificial vision system and force acoustic sensors; P. Fillatreau et al.; Robotics and Computer-Integrated Manufacturing 24 (2008) 780-787, describes a multi sensor approach, incorporating artificial vision. The system is customised to analyse a particular type of part, at a rate of 2 parts per second. This control system combines fuzzy logic and expert system techniques, giving the operator of the machine feedback and advice on possible errors and advice on how to correct them. To do that, it is assumed that after getting the correct process setup and at the beginning of the production, parts are defect-free. During this phase, signals are recorded and an upper and lower envelope curves are determined that distinguish good parts from defect parts. The system is then trained to recognise when a signal is outside this envelope and in this case indicate a faulty part. The paper provides significant tools for the real time identification of defective parts as well as for the optical determination of shapes and geometries. It is nevertheless unclear how the measurements are linked to process parameters and, in effect, how the feedback control is achieved. Further, it is based on historical data and ignores natural process drift and variation that may not result in defective parts. It is not possible to identify particular defects, their location and severity. Finally it cannot be used during try-outs.
Optical Measuring Technologies in Sheet Metal Processing; K. Galanulis; Advanced Materials Research Vols. 6-8 (2005) pp 19-34, describes an optical system for the scanning of 3D surfaces, that is, for obtaining measurements of the location of surface points in 3D space. Based on measurements from a section of deformed sheet metal, strain, thickness reduction and local hardening may be calculated. From these, excessive strain and material faults can be determined.
Contactless on-line measurement of material flow for closed loop control of deep drawing; E Doege et al: Journal of Materials Processing Technology 130-131 (2002) 95-99: A new optical sensor for contactless online material flow measurement is presented. The sensor may be incorporated in a deep-drawing tool and observe material flow online during the forming process.
Numerical Methods and Hardware Components for an Adaptive Robustness Control During the Production of Stamped Parts; Manopulo et al, Numisheet 2008 pp. 871-876. A feasible way of using stochastic FE simulations along with eddy-current material testing in order to achieve online control of the scattering of material properties is presented. In a training step destructive and non-destructive tests are used to measure mechanical properties of batches of blanks. The stochastic simulation and these measurements are then used to create a mathematical model that can discriminate between blanks that will result in defects and blanks that should go to production. Alternatively, process parameters could be adjusted to account for the material properties. Manopulo et al attempt to prevent quality problems by assuming that the material properties are the main driver of defects. Their method applies to accepting and rejecting blanks during production, before the stamping process, employing a typical forward (input to output) usage of the stochastic simulation. It does not apply to tryout support. Most importantly it assumes that process parameters are perfectly controllable and identical through time and only material parameters determine the outcome of the process, which they do not consider at all.
CAE tools as a valid opportunity to improve quality control systems performances for sheet metal formed components; A. Del Prete et al.; 9th Biennal ASME Conf on Engineering Systems Design and Analysis ESDA08 (2008) 329-334. Stochastic simulation of a deep drawing process is described. An example is given wherein the maximum binder force is computed by simulation, and its dependency on a number of design variables is presented. It is conjectured that the information from such a stochastic study would help to determine geometric features that have a high probability of drift and should therefore be monitored, e.g. by an optical scanner. The paper clearly recognizes the possibilities of the stochastic simulation as a source of information for the online quality control. However, the method cannot identify defects other than mismatches between CAD geometry and actual part. Further, it does not use the simulation to provide feedback about process conditions as there is no link between the process and the stochastic simulation. Finally, the method, if implemented, could have limited use in quality control as it cannot identify defects other than geometry divergence whereas excessive thinning and cracks, wrinkles and surface defects may be equally or more interesting. Finally it does not apply to tryout.